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Record W4407447937 · doi:10.1126/science.adr5926

Good plasmons in a bad metal

2025· article· en· W4407447937 on OpenAlexaff
Francesco L. Ruta, Yinming Shao, Swagata Acharya, Anqi Mu, Na Hyun Jo, Sae Hee Ryu, Daria A. Balatsky, Yifan Su, Dimitar Pashov, Brian S. Y. Kim, M. I. Katsnelson, James G. Analytis, Eli Rotenberg, Andrew J. Millis, Mark van Schilfgaarde, D. N. Basov

Bibliographic record

VenueScience · 2025
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsCanadian Quantum Research Center
FundersU.S. Department of Energy
KeywordsPlasmonQuasiparticleCondensed matter physicsPhysicsSurface plasmonPolaritonAnisotropyvan der Waals forceSurface plasmon polaritonOpticsQuantum mechanicsSuperconductivity

Abstract

fetched live from OpenAlex

Correlated metals may exhibit unusually high resistivity that increases linearly in temperature, breaking through the Mott-Ioffe-Regel bound, above which coherent quasiparticles are destroyed. The fate of collective charge excitations, or plasmons, in these systems is a subject of debate. Several studies have suggested that plasmons are overdamped, whereas other studies have detected propagating plasmons. In this work, we present direct nano-optical images of low-loss hyperbolic plasmon polaritons (HPPs) in the correlated van der Waals metal MoOCl 2 . HPPs are plasmon-photon modes that waveguide through extremely anisotropic media and are remarkably long-lived in MoOCl 2 . Photoemission data presented here reveal a highly anisotropic Fermi surface, reconstructed and made partly incoherent, likely through electronic interactions as explained by many-body theory. HPPs remain long-lived despite this, revealing previously unseen imprints of many-body effects on plasmonic collective modes.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.459
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.266
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations32
Published2025
Admission routes1
Has abstractyes

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